code: def _lowest_confidence(
      x,
      fun_prediction,
      fun_prediction_args,
      x_bounds,
      x_types,
      minimize_constraints_fun,
  ):
      # This is only for step-wise optimization
      x = lib_data.match_val_type(x, x_bounds, x_types)
  
      ci = sys.maxsize
      if (minimize_constraints_fun is None) or (
          minimize_constraints_fun(x) is True
      ):
          mu, sigma = fun_prediction(x, *fun_prediction_args)
          ci = (sigma * 1.96 * 2) / mu
          # We want ci to be as large as possible
          # (i.e., as small as possible for minimize(...),
          # because this would mean lowest confidence
          ci = -1 * ci
  
      return ci
  
file_path: nni/nni/algorithms/hpo/metis_tuner/lib_acquisition_function.py
imports: import sys
  import numpy
  from scipy.stats import norm
  from scipy.optimize import minimize
  from .None import lib_data
label_desc: Use image node_id 4 for calling a global function with example usage: _lowest_confidence(x, fun_prediction, fun_prediction_args, x_bounds, x_types, minimize_constraints_fun) and returns: ci